Early biomarkers of Parkinsonís disease based on natural connected speech The aim is to predict a pattern of neurodegeneration in the dataset of speech features obtained from patients with early untreated Parkinsonís disease and patients at high risk developing Parkinsonís disease.
The dataset include 30 patients with early untreated Parkinsonís disease (PD), 50 patients with REM sleep behaviour disorder (RBD), which are at high risk developing Parkinsonís disease or other
synucleinopathies; and 50 healthy controls (HC). All patients were scored clinically by a well-trained professional neurologist with experience in movement disorders. All subjects were examined during a
single session with a speech specialist. All subjects performed reading of standardized, phonetically-balanced text of 80 words and monologue about their interests, job, family or current activities for
approximately 90 seconds. Speech features were automatically analysed using developed algorithm.
[Excel]

More details can be found if following paper, please include the citation if you use it in your work: